--- license: apache-2.0 base_model: - Qwen/Qwen2-VL-2B-Instruct --- # Requirements This is compatible with any onnx runtime. # Running this model **Javascript** See https://huggingface.co/spaces/pdufour/Qwen2-VL-2B-Instruct-ONNX-Q4-F16 for a demo. **Python** ``` import time import torch import numpy as np import onnxruntime from PIL import Image import os import sys import requests from io import BytesIO try: from export_config import INPUT_IMAGE_SIZE, IMAGE_RESIZE, MAX_SEQ_LENGTH, HEIGHT_FACTOR, WIDTH_FACTOR except: INPUT_IMAGE_SIZE = [960, 960] HEIGHT_FACTOR = 10 WIDTH_FACTOR = 10 IMAGE_RESIZE = [HEIGHT_FACTOR * 28, WIDTH_FACTOR * 28] MAX_SEQ_LENGTH = 1024 path = sys.argv[1] script_dir = sys.argv[2] onnx_model_A = os.path.join(script_dir, 'QwenVL_A.onnx') onnx_model_B = os.path.join(script_dir, 'QwenVL_B_q4f16.onnx') onnx_model_C = os.path.join(script_dir, 'QwenVL_C_q4f16.onnx') onnx_model_D = os.path.join(script_dir, 'QwenVL_D_q4f16.onnx') onnx_model_E = os.path.join(script_dir, 'QwenVL_E_q4f16.onnx') print("\n[PATHS] ONNX model paths:") print(f" Model A: {onnx_model_A}") print(f" Model B: {onnx_model_B}") print(f" Model C: {onnx_model_C}") print(f" Model D: {onnx_model_D}") print(f" Model E: {onnx_model_E}") image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg" query = "Describe this image." from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer with torch.inference_mode(): model = Qwen2VLForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float32, device_map="mps", low_cpu_mem_usage=True) max_seq_len = MAX_SEQ_LENGTH num_heads = model.config.num_attention_heads num_key_value_heads = model.config.num_key_value_heads head_dim = model.config.hidden_size // num_heads num_layers = model.config.num_hidden_layers hidden_size = model.config.hidden_size max_single_chat_length = 12 tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) session_opts = onnxruntime.SessionOptions() session_opts.log_severity_level = 3 session_opts.inter_op_num_threads = 0 session_opts.intra_op_num_threads = 0 session_opts.enable_cpu_mem_arena = True session_opts.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL session_opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL session_opts.add_session_config_entry("session.intra_op.allow_spinning", "1") session_opts.add_session_config_entry("session.inter_op.allow_spinning", "1") ort_session_A = onnxruntime.InferenceSession(onnx_model_A, sess_options=session_opts) ort_session_B = onnxruntime.InferenceSession(onnx_model_B, sess_options=session_opts) ort_session_C = onnxruntime.InferenceSession(onnx_model_C, sess_options=session_opts) ort_session_D = onnxruntime.InferenceSession(onnx_model_D, sess_options=session_opts) ort_session_E = onnxruntime.InferenceSession(onnx_model_E, sess_options=session_opts) in_name_A = ort_session_A.get_inputs() out_name_A = ort_session_A.get_outputs() in_name_A0 = in_name_A[0].name out_name_A0 = out_name_A[0].name in_name_B = ort_session_B.get_inputs() out_name_B = ort_session_B.get_outputs() in_name_B0 = in_name_B[0].name in_name_B1 = in_name_B[1].name out_name_B0 = out_name_B[0].name in_name_C = ort_session_C.get_inputs() out_name_C = ort_session_C.get_outputs() in_name_C0 = in_name_C[0].name out_name_C0 = out_name_C[0].name in_name_D = ort_session_D.get_inputs() out_name_D = ort_session_D.get_outputs() in_name_D0 = in_name_D[0].name in_name_D1 = in_name_D[1].name in_name_D2 = in_name_D[2].name in_name_D3 = in_name_D[3].name in_name_D4 = in_name_D[4].name out_name_D0 = out_name_D[0].name out_name_D1 = out_name_D[1].name in_name_E = ort_session_E.get_inputs() out_name_E = ort_session_E.get_outputs() in_name_E0 = in_name_E[0].name in_name_E1 = in_name_E[1].name in_name_E2 = in_name_E[2].name in_name_E3 = in_name_E[3].name in_name_E4 = in_name_E[4].name in_name_E5 = in_name_E[5].name in_name_E6 = in_name_E[6].name in_name_E7 = in_name_E[7].name out_name_E0 = out_name_E[0].name out_name_E1 = out_name_E[1].name out_name_E2 = out_name_E[2].name response = requests.get(image_url) image = Image.open(BytesIO(response.content)) if image.mode != 'RGB': image = image.convert('RGB') pixel_values = np.transpose(np.array(image).astype(np.float32), (2, 0, 1)) pixel_values = np.expand_dims(pixel_values, axis=0) / 255.0 use_vision = True prompt = f"\n<|im_start|>user\n<|vision_start|><|vision_end|>{query}<|im_end|>\n<|im_start|>assistant\n" prompt_head_len = np.array([5], dtype=np.int64) image_embed_size = WIDTH_FACTOR * HEIGHT_FACTOR token = tokenizer(prompt, return_tensors='pt')['input_ids'] ids_len = np.array([token.shape[1]], dtype=np.int64) input_ids = np.zeros(max_seq_len, dtype=np.int32) input_ids[:ids_len[0]] = token[0, :] history_len = np.zeros(1, dtype=np.int64) past_key_states = np.zeros((num_layers, num_key_value_heads, max_seq_len, head_dim), dtype=np.float16) past_values_states = past_key_states attention_mask = np.array([-65504.0], dtype=np.float16) pos_factor = np.array([0.0], dtype=np.float16) pos_factor_v = 1 - image_embed_size + WIDTH_FACTOR dummy = np.array(0, dtype=np.int32) hidden_states = ort_session_B.run( [out_name_B0], { in_name_B0: input_ids, in_name_B1: ids_len })[0] position_ids, = ort_session_C.run( [out_name_C0], { in_name_C0: dummy }) if use_vision: image_embed = ort_session_A.run( [out_name_A0], {in_name_A0: pixel_values})[0] ids_len += image_embed_size split_factor = np.array(max_seq_len - ids_len[0] - image_embed_size, dtype=np.int32) ids_len_minus = np.array(ids_len[0] - prompt_head_len[0], dtype=np.int32) hidden_states, position_ids = ort_session_D.run( [out_name_D0, out_name_D1], { in_name_D0: hidden_states, in_name_D1: image_embed, in_name_D2: ids_len, in_name_D3: ids_len_minus, in_name_D4: split_factor }) end_time = time.time() end_time = time.time() num_decode = 0 while (num_decode < max_single_chat_length) & (history_len < max_seq_len): token_id, past_key_states, past_values_states = ort_session_E.run( [out_name_E0, out_name_E1, out_name_E2], { in_name_E0: hidden_states, in_name_E1: attention_mask, in_name_E2: past_key_states, in_name_E3: past_values_states, in_name_E4: history_len, in_name_E5: ids_len, in_name_E6: position_ids, in_name_E7: pos_factor }) if (token_id == 151643) | (token_id == 151645): break else: num_decode += 1 if num_decode < 2: history_len += ids_len[0] ids_len[0] = 1 attention_mask = np.array([0.0], dtype=np.float16) if use_vision: pos_factor = np.array(pos_factor_v + ids_len[0], dtype=np.float16) else: pos_factor = np.array(history_len[0] + 1, dtype=np.float16) else: history_len += 1 pos_factor += 1 input_ids[0] = token_id hidden_states = ort_session_B.run( [out_name_B0], { in_name_B0: input_ids, in_name_B1: ids_len })[0] decoded_token = tokenizer.decode(token_id) print(f"Decoded token: {decoded_token}") print(decoded_token, end="", flush=True) generation_time = time.time() - end_time ``` # Technical Information: - [EXPORT.md](EXPORT.md)